When considering textual data the Hamming distance would not consider the frequently occurring words in the document and would be responsible for yielding a lower similarity index from the text document while cosine similarity considers the frequently occurring words in the text document and will help in yielding higher similarity scores for the text data. But hamming distance considers only the character type of data of the same length but cosine similarity has the ability to handle variable length data.
The cosine similarity measure operates entirely on the cosine principles where with the increase in distance the similarity of data points reduces.Įvalueserve is certified as a Best Firm For Data ScientistsĪmong all these popular metrics for distance calculation and when considered for classification or text data instead of cosine similarity, Hamming distance can be used as a metric for KNN, recommendation systems, and textual data. Use of cosine similarity with textual dataĬosine similarity is the cosine of the angle between two vectors and it is used as a distance evaluation metric between two points in the plane.
Cosine similarity is a measure of similarity between two data points in a plane.
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